Joint Learning of Named Entity Recognition and Entity Linking

By Pedro Henrique Martins, Zita Marinho, André F. T. Martins. 2019

103 citations recorded by the source corpus.

Named entity recognition (NER) and entity linking (EL) are two fundamentally related tasks, since in order to perform EL, first the mentions to entities have to be detected. However, most entity linking approaches disregard the mention detection part, assuming that the correct mentions have been previously detected. In this paper, we perform joint learning of NER and EL to leverage their relatedness and obtain a more robust and generalisable system. For that, we introduce a model inspired by the Stack-LSTM approach. We observe that, in fact, doing multi-task learning of NER and EL improves the performance in both tasks when comparing with models trained with individual objectives. Furthermore, we achieve results competitive with the state-of-the-art in both NER and EL.

Subfields: Artificial Intelligence, Natural Language Processing, Information Extraction

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yuyang Nie
2. Bo Dai
3. Feng Jiang
4. Yuanhe Tian
5. Jinpeng Wang
6. Yan Song
7. Xiang Wan
8. Shuang Chen

In Natural Language Processing

1. Yuyang Nie
2. Bo Dai
3. Feng Jiang
4. Yuanhe Tian
5. Jinpeng Wang
6. Yan Song
7. Xiang Wan
8. Shuang Chen

In Information Extraction

1. Bo Dai

Citations in the corpus

• Autoregressive Entity Retrieval (2020, neutral): “5 46.8 18.1 20.5 46.2 46.4 34.7 Moro et al. (2014) 48.5 39.7 29.8 55.9 23.0 29.1 41.9 37.7 38.2 Kolitsas et al. (2018) 82.4 72.4 34.1 35.2 50.3 38.2 61.9 52.7 53.4 Broscheit (2019) 79.3 - - - - - - - Martins et al. (2019) 81.9 - - - - - - - van Hulst et al. (2020)y 80.5 72.4 41.1 50.7 49.9 35.0 63.1”
• Improving Entity Linking by Modeling Latent Entity Type Information (2020, background): “Recently, Martins, Marinho, and Martins (2019) perform multi-task learning using learned features by extending Stack-LSTM (Dyer et al. 2015).”
• Named Entity Recognition for Social Media Texts with Semantic Augmentation (2020, background): “Thus, named entity recognition (NER) is a critical step for detecting proper entities in texts and providing support for down-stream natural language processing (NLP) tasks (Pang et al., 2019; Martins et al., 2019).”